Token Classification
spaCy
Spanish
named-entity-recognition
spanish
music
digital-humanities
historical-text
bert
Eval Results (legacy)
Instructions to use LexiMusUSAL/LexiMus-BETO-per-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use LexiMusUSAL/LexiMus-BETO-per-v1 with spaCy:
!pip install https://huggingface.co/LexiMusUSAL/LexiMus-BETO-per-v1/resolve/main/LexiMus-BETO-per-v1-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("LexiMus-BETO-per-v1") # Importing as module. import LexiMus-BETO-per-v1 nlp = LexiMus-BETO-per-v1.load() - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0215b5951534243607d12275397793fc8feb103e132c639acf1eb8861398a661
- Size of remote file:
- 1.33 MB
- SHA256:
- dadeaf16db1cfeb79fc7abd380046186b065ffdcb2e7ec45e6af5ae07e3accb0
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